Three ways big data can benefit your business
Two industries, two customers, three big uses of high-performance analytics
At the simplest level, advanced analytics allows you to develop models and then use them to ask what-if questions about your data. For example, developing a statistical model that associates buying behavior with customer profiles can then be applied to future behavior of customers. The application of that model is referred to as "scoring" and is the basis for predictive analytics.
That type of analysis is worlds away from traditional business intelligence, which is more about asking simple questions about data in one or two dimensions (e.g., how many shoes of Brand X do we have in stock?). That kind of analysis is fairly straightforward using a traditional database, needing only a small pipe to get the data in and out and a software component on the client to manage the interface.
Combining big data with predictive analytics can be a challenge for many industries, but high-performance analytics, which speeds the process of scoring and reporting, is helping SAS customers in many areas.
Here, we explore three:
Detect, prevent and remediate financial fraud
As the volume and sophistication of these schemes increases, many organizations are turning to powerful analytics to sift through massive data volumes and uncover hidden patterns, trends and suspicious events that can indicate criminal fraud.
In most instances, fraud detection involves analyzing the various attributes of transactions and making a determination about whether those orders should be flagged for further review. But as volumes increase, the thresholds for intervention or review increase, meaning there's a greater likelihood of fraudulent transactions eluding scrutiny.
For one SAS customer – a major global manufacturer of consumer electronics – the challenge of stemming losses attributable to fraud had overwhelmed its previous infrastructure, and a new approach was needed to scale up to the increasing demands of fighting fraud.
Although the company had numerous server instances of SAS for a wide variety of reporting and analysis, there was only one server dedicated to fraud analytics. As a result, the fraud-fighting initiative was hampered by limits in compute resources, storage and overall performance, as well as a limited ability to refine the predictive abilities of the fraud models.
The project team laid out a plan and process for transitioning from a series of disconnected departmental data marts to a true enterprise-scale analytic data warehouse. Thanks to SAS software's in-database processing capabilities, instead of bringing the data to the application, the IT organization can bring the application to the database and perform the analytics and statistical functions within the database itself.
This means the models run operationally across all of the company's customers, as opposed to extracting data from the environment, processing the data, and importing the results back into the warehouse.
Using SAS Grid Computing, the manufacturer's fraud and IT teams were able to more efficiently collaborate on their efforts to improve their fraud-detection models. That enables them to keep up with fraudsters and their evolving schemes, achieve a better "model lift" – the ability to trap more fraudulent transactions correctly – and scan more transactions, which ultimately leads to preventing more instances of fraud before they can have a financial impact on the company.
Calculate risk on a large portfolio of loans
For one major SAS customer in the financial services market, one of the root causes of unacceptable risk exposure was simply an inability to efficiently create models and run those models against its growing data volumes. The institution was capturing data at a rate that was far faster than its ability to compute that data in a timely fashion. Literally, the company's modeling team couldn't work fast enough to meet the demand for new and refined models.
The company faced unacceptable risks and unacceptable processing times in trying to create and run models to minimize that risk. Seeking a better way, the firm pursued and deployed a new paradigm for its analytical processing: high-performance analytics.
The performance improvements were extraordinary. Instead of waiting a week to execute a new model and assess the risk contained in the portfolio of consumer mortgages, the firm can now generate the same results in only 84 seconds. This gives time and motivation to analysts to iterate their models many more times than before. Since the firm is managing a loan portfolio of billions of dollars, even a modest improvement translates into savings in the tens of millions of dollars.
Execute high-value marketing campaigns
This company operates a sophisticated marketing operation, running campaigns to millions of targets. However, as the data volumes grew and the campaigns began to target 10 million to 15 million recipients, it couldn't physically process the data, preventing the company from maximizing its customer lifetime value and executing more efficient and effective cross-sell/up-sell campaigns.
Using high-performance analytics, the company has achieved tremendous gains in the throughput of its database marketing – as much as 215 times faster – dramatically compressing the model development life cycle and enabling its teams to test and validate additional variables for greater reliability in their models.
High-performance analytics removes the limits on observations and variables that the company can process. That opened up the scope of questions to ask and avenues to pursue.
The result is that the team's productivity in executing the models for the campaigns was dramatically improved. More importantly, the effectiveness and predictive reliability of the marketing models improved as well.
Given the volume of data, even small incremental improvements create significant savings. For instance, a typical direct mail campaign usually generates a 1 percent response rate. When sending to 15 million prospects with a lifetime customer value of $500, a fractional improvement in response rates for cross-sell or up-sell offers quickly translates into tens of millions of dollars annually in top-line revenue.
High-performance analytics makes the difference
This story appears in the First Quarter 2013 issue of